POS1531-HPR PATIENT-REPORTED ADVERSE DRUG REACTIONS ATTRIBUTED TO THE USE OF ETANERCEPT: DISTINCTION BASED ON NATURE, FREQUENCY AND BURDEN
Authors: L. Van Boxem, H. Gosselt, S. Tas, B. Van den Bemt, H. Vonkeman, F. Hoentjen, M. Nurmohamed, M. Van Doorn, N. Jessurun
Journal: Annals of the Rheumatic Diseases (2022)

Background Research regarding adverse drug reactions (ADRs) associated with the use of etanercept in patients with inflammatory rheumatic diseases (IRDs) usually focuses on the nature and frequency of ADRs without considering the burden of the ADRs. However, not every ADR causes the same burden for patients. Information is lacking about the degree of experienced burden per ADR by patients with IRDs. Objectives First, to describe ADRs of etanercept based on nature, frequency and burden, and second, to propose a new model for identification of relevant ADRs for health care professionals. Methods Data of the Dutch Biologic Monitor (DBM) was used to categorize patient-reported ADRs into high and low burden. In this prospective cohort event monitoring system patients were asked to fill out bimonthly questionnaires on experienced ADRs that they attributed to the use of a biological DMARD. The questionnaire included a quantification of the burden of the reported ADRs using a five-point Likert scale ranging from 1 (no burden) to 5 (very high burden). The nature of the reported ADRs were grouped into preferred terms (PTs) according to the Medical Dictionary for Regulatory Activities (MedDRA). Inclusion criteria for this study were patients with IRDs using etanercept and who reported an ADR with at least one burden score. For every patient, the mean burden scores per ADR (MedDRA PT) were analyzed. The burden was classified in two categories: ‘high’ when the mean burden score was equal to or more than 2.5, and ‘low’ when it was less than 2.5. Text analytics of the reported ADRs (MedDRA PTs) and a comparison word cloud were used to visualize ADRs that were more often reported with high burden or more often reported with low burden. For this, the relative ADR frequencies of the low burden classes were subtracted from the relative frequencies of the high burden class, resulting in a percentual difference between the high and low burden class for every ADR. Therefore, the highest percentual difference corresponds to the ADRs that are experienced as most burdensome. Results A total of 187 patients (70% female) met the inclusion criteria and reported 905 ADRs, of which 373 (41%) were reported with high burden (see Table 1). The word cloud (Figure 1) visualizes which ADRs were more often reported with high or low burden. These ADRs correspond to the greatest difference in relative class frequency. Patients experienced the burden of headache, pneumonia and pruritis mainly as high, and the burden of injection site pruritus and injection site erythema as low. Table 1. Characteristics of patients and reported adverse drug reactions (ADRs) Characteristics Total Patients (n = 187) Gender (female, %) 130 (70) Age (years) (mean ± SD) 58.1 ± 14.1 Indication a Rheumatoid arthritis (%) 132 (71) Ankylosing spondylitis/axial spondyloarthritis (%) 19 (10) Psoriatic arthritis (%) 46 (25) Adverse drug reactions Total High burden Low burden Adverse drug reactions (ADRs) (%) 905 (100) 373 (40) 532 (60) Drug-induced ADR burden (mean ± SD) 2.5 ± 0.9 3.3 ± 0.7 1.9 ± 0.5 High burden ADRs: Headache 30 22 8 Pruritus 23 17 6 Pneumonia 20 15 5 Feeling cold 11 11 0 Skin atrophy 11 11 0 Figure 1. Comparison word cloud visualizing adverse drug reactions (ADRs) that patients reported more often with high burden (orange) and with no to low burden (blue). An ADR increases in size if the relative difference in frequency between classes increases. Conclusion The outcomes of the word cloud reveal that headache is more often experienced as burdensome which is an unexpected outcome. Visualizing the nature, the frequency and the burden of ADRs in one picture, provides simple guidance to the degree of relevance for the reported ADRs in clinical practice. Disclosure of Interests Larissa van Boxem: None declared, Helen Gosselt: None declared, Sander Tas Consultant of: Gebro, GSK, AbbVie, Galvani, Arthrogen/MeiraGTx, Galapagos, Grant/research support from: Pfizer, GSK, Celgene, BMS, Sanofi, AstraZeneca, Bart van den Bemt Speakers bureau: paid as speaker for UCB, Pfizer, Sanofi-Aventis, Galapagos, Amgen en Eli Lilly, Harald Vonkeman Speakers bureau: Amgen, BMS, Celgene, Galapagos, GSK, Janssen-Cilag, Lilly, Novartis, Pfizer, Roche, Sanofi-Genzyme, UCB, Grant/research support from: Abbvie, Sanofi-Genzyme, Frank Hoentjen Speakers bureau: Frank Hoentjen has served on advisory boards or as speaker for Abbvie, Janssen-Cilag, MSD, Takeda, Celltrion, Teva, Sandoz and Dr Falk, Consultant of: Celgene, Michael Nurmohamed Speakers bureau: Abbvie, Janssen, Celgene, Consultant of: Abbvie, Grant/research support from: Abbvie, Amgen, Pfizer, Galapagos, BMS, Martijn van Doorn Speakers bureau: Janssen, LEO Pharma, Pfizer, Novartis, Paid instructor for: LEO Pharma, Consultant of: AbbVie, Janssen, LEO Pharma, Pfizer, Celgene, Novartis, TEVA, MSD, Sanofi, AstraZeneca, Grant/research support from: Novartis, Janssen, Naomi Jessurun: None declared